ArticleBioengineering (Basel, Switzerland)2023
A Novel Baseline Removal Paradigm for Subject-Independent Features in Emotion Classification Using EEG.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 51 citations in OpenAlex.
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- Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic-Ischemic Encephalopathy Injury.Sensors (Basel, Switzerland) · 2025Article
- Interpretable manifold learning for T-wave alternans assessment with electrocardiographic imaging.Engineering applications of artificial intelligence · 2025Article
- Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine.Scientific reports · 2025Article
- Analysis of frequency domain features for the classification of evoked emotions using EEG signals.Experimental brain research · 2025Article
- The Problem with Time: Application of Partial Least Squares Analysis on Time-Frequency Plots to Account for Varying Time Intervals with Applied EEG Data.Brain sciences · 2025Article
- Employing large language models for emotion detection in psychotherapy transcripts.Frontiers in psychiatry · 2025Article
- Measuring emotions triggered by fragrance through behavioral paradigms.Frontiers in psychology · 2025Article
- Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface.Bioengineering (Basel, Switzerland) · 2023Article
- Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals.Sensors (Basel, Switzerland) · 2023Article
- Attention-based 3D convolutional recurrent neural network model for multimodal emotion recognition.Frontiers in neuroscience · 2023Article
- Development of depression detection algorithm using text scripts of routine psychiatric interview.Frontiers in psychiatry · 2023Article
- Identifying relevant asymmetry features of EEG for emotion processing.Frontiers in psychology · 2023Article
- Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals.Frontiers in psychology · 2023Article
- An innovative brain-inspired neuronal competition model for electroencephalography emotion recognition.Digital healthArticle
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotion plays a vital role in understanding the affective state of mind of an individual. In recent years, emotion classification using electroencephalogram (EEG) has emerged as a key element of affective computing. Many researchers have prepared datasets, such as DEAP and SEED, containing EEG signals captured by the elicitation of emotion using audio-visual stimuli, and many studies have been conducted to classify emotions using these datasets. However, baseline power removal is still considered one of the trivial aspects of preprocessing in feature extraction. The most common technique that prevails is subtracting the baseline power from the trial EEG power. In this paper, a novel method called InvBase method is proposed for removing baseline power before extracting features that remain invariant irrespective of the subject. The features extracted from the baseline removed EEG data are then used for classification of two classes of emotion, i.e., valence and arousal. The proposed scheme is compared with subtractive and no-baseline-correction methods. In terms of classification accuracy, it outperforms the existing state-of-art methods in both valence and arousal classification. The InvBase method plus multilayer perceptron shows an improvement of 29% over the no-baseline-correction method and 15% over the subtractive method.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.